Abstract
MATLAB is a high-level language used in various scientific and engineering fields. Deployment of well-Tested MATLAB code to production would be highly desirable, but in practice a number of obstacles prevent this, notably performance and portability. Although MATLAB-To-C compilers exist, the performance of the generated C code may not be sufficient and thus it is important to research alternatives, such as CPU parallelism, GPGPU computing and FPGAS. OpenCL is an API and programming language that allows targeting these devices, hence the motivation for MATLAB-To-OpenCL compilation. In this paper, we describe our recent efforts on offloading code to OpenCL devices in the context of our MATLAB to C/OpenCL compiler. © 2017 ACM.

Abstract
ProcessPAIR is a novel tool for automating the performance analysis of software developers. Based on a performance model calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. We present the results of a controlled experiment involving 61 software engineering master students, half of whom used ProcessPAIR in a performance analysis assignment. The results show significant benefits in terms of students' satisfaction (average score of 4.78 out of 5 for ProcessPAIR users, against 3.81 for other users), quality of the analysis outcomes (average grades achieved of 88.1 out of 100 for ProcessPAIR users, against 82.5 for other users), and time required to do the analysis (average of 252 min for ProcessPAIR users, against 262 min for other users, but with much room for improvement).

Abstract
Assessing the quality of aperture synthesis maps is relevant for benchmarking image reconstruction algorithms, for the scientific exploitation of data from optical long-baseline interferometers, and for the design/upgrade of new/existing interferometric imaging facilities. Although metrics have been proposed in these contexts, no systematic study has been conducted on the selection of a robust metric for quality assessment. This article addresses the question: what is the best metric to assess the quality of a reconstructed image? It starts by considering several metrics and selecting a few based on general properties. Then, a variety of image reconstruction cases are considered. The observational scenarios are phase closure and phase referencing at the Very Large Telescope Interferometer (VLTI), for a combination of two, three, four and six telescopes. End-to-end image reconstruction is accomplished with the MIRA software, and several merit functions are put to test. It is found that convolution by an effective point spread function is required for proper image quality assessment. The effective angular resolution of the images is superior to naive expectation based on the maximum frequency sampled by the array. This is due to the prior information used in the aperture synthesis algorithm and to the nature of the objects considered. The l(1)-norm is the most robust of all considered metrics, because being linear it is less sensitive to image smoothing by high regularization levels. For the cases considered, this metric allows the implementation of automatic quality assessment of reconstructed images, with a performance similar to human selection.

Abstract
This paper presents some teaching strategies applied in a curricular unit of the 3rd year of the Integrated Master of Engineering and Industrial Management at the University of Minho The goal was to teach theoretical concepts associated to automation topics. The practical aspects as designing an automated machine or developing the corresponding Ladder Logic diagram is often well accepted by students. On the other hand, topics related to instrumentation, sensor type and functioning for example, more theoretical, are usually less attractive to students. So, different tools have been developed and tested along the past years to overcome this concern. This paper presents the last experience tested: evaluating a previously developed APP Inventor tool (developed by students) as wondering if it is a suitable way to learn automation; an automation conquest, where the competition may promote the stimulus to learn.

Abstract

Abstract
We present a hierarchical harmonic mixing method for assisting users in the process of music mashup creation. Our main contributions are metrics for computing the harmonic compatibility between musical audio tracks at small- and large-scale structural levels, which combine and reassess existing perceptual relatedness (i.e., chroma vector similarity and key affinity) and dissonance-based approaches. Underpinning our harmonic compatibility metrics are harmonic indicators from the perceptually-motivated Tonal Interval Space, which we adapt to describe musical audio. An interactive visualization shows hierarchical harmonic compatibility viewpoints across all tracks in a large musical audio collection. An evaluation of our harmonic mixing method shows our adaption of the Tonal Interval Space robustly describes harmonic attributes of musical instrument sounds irrespective of timbral differences and demonstrates that the harmonic compatibility metrics comply with the principles embodied in Western tonal harmony to a greater extent than previous approaches. © 2018, Springer Nature Switzerland AG.